This poster abstract presents a case study of charging Electric Vehicles (EVs) at home, taking into consideration the household power consumption and the vehicle driving routines of the residents. It reveals some challenges of charging EVs in the household and highlights the importance of proper charging scheduling in order to avoid potential tripping of the household circuit breaker.

This paper describes recent work on the development of a wireless-based remote monitoring system for household energy consumption and generation in Madeira Island, Portugal. It contains three different main sections: (1) a monitoring system for consumed and produced energy of residencies equipped with photovoltaic (PV) systems, (2) developing a tool to predict the electricity production, (3) and proposing a solution to detect the PV system malfunctions. With the later tool, the user (owner) or the energy management system can monitor its own PV system and make an efficient schedule use of electricity at the consumption side. In addition, currently, the owners of PV systems are notified about a failure in the system only when they receive the bill, whereas using the proposed method conveniently would notify owners prior to bill issue. The artificial neural network was employed as a tool together with the hardware-based monitoring system which allows a daily analysis of the performance of the system. The comparison of the predicted value of the produced electricity with the actual production for each day shows the validity of the method.

Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.

Non-intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large-scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross-datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology. This article is categorized under: Application Areas > Science and Technology Application Areas > Data Mining Software Tools Technologies > Computational Intelligence Technologies > Machine Learning

In this work, we analyse experimentally the be- haviour of 18 different performance metrics when applied to classification algorithms in event-based Non-Intrusive Load Monitoring, identifying relationships and clusters between the measures.

Our results indicate that performance metrics have more in common than what was initially expected. Our results also suggest that in this multi-class classification problem, researchers should avoid micro-average and unweighted macro-average met- rics in favor of their weighted macro-average counterparts. Finally, the results also suggest that probabilistic measures can provide important information that is not available when using more traditional performance metrics.

It is a great honour and pleasure to welcome you to the Fifth IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT 2017) in Funchal, Portugal.

As a multidisciplinary conference devoted to enhance the sustainability of ICT and via ICT, SustainIT 2017 focuses on three main themes: 1) SUSTAINABLE INTERNET AND ICT, including novel standards and metrics for green communications, and the measurement and evaluation of the Internet’s sustainability; 2) SUSTAINABILITY THROUGH THE APPLICATION OF ICT, including ICT for smart grids, smart water distribution systems, and ICT for energy efficiency in smart homes and buildings; and 3) HUMAN-CENTERED TECHNOLOGY FOR SUSTAINABILITY, including user evaluation of test-bed and prototype implementations, and human-factors in sustainable ICT systems.

SustainIT 2017 is intended to bring together people from different research areas, and provide a forum to exchange ideas, discuss solutions, and share experiences among researchers, professionals, and application developers from both industry and academia.

This year we received 30 original research paper submissions, and 13 work-in-progress submissions. Each submission was subject to peer review by the members of the Technical Program Committee. Each paper was reviewed by at least three expert reviewers, whereas each work-in-progress paper was reviewed by two expert reviewers. In the end, we selected 6 full papers and 6 short papers to be presented in four technical sessions, and 10 work-in-progress papers to be presented in a dedicated poster and demos session.

SustainIT 2017 also featured a PhD Forum, and two keynote lectures delivered by internationally renowned speakers. Professors Carla Gomes, from Cornell University, and António Câmara, from the New University of Lisbon. We would like to take this opportunity to thank the keynote speakers for their valuable contribution to the conference.

We would like to take this opportunity to thank all those who worked so hard to make this conference a success. We sincerely thank all members of the Organizing Committee for their long term enthusiasm in organizing the conference, including WIP / Demo Co-Chairs, Anna Förster and Vlad Coroama; PhD Forum Co-Chairs, Marco Blumendorf, and Ricardo Lima; Creative and Artistic Interventions Co-Chairs, Mónica Mendes and Valentina Nisi; Publication Chair, Nuno Pereira; Local Organization Co-Chairs, Filipe Quintal and Mary Barreto; Image and Communications Chair, Sabrina Scuri; Student Volunteers Chair, Afonso Gonçalves, all the student volunteers, and World Travel for all their support to the realization of SustainIT 2017.

We are grateful to all members of the Technical Program Committee who offered their time and expertise to provide high-quality reviews.

Finally, we thank IFIP, including the Technical Committee 6, Working Group 6.3 Performance of Computer Communication Systems, and the IEEE Computer Society including IEEE Technical Committee on Computer Communications for their technical co-sponsorship.

Special thanks to the Madeira Promotion Bureau, Exictos and prsma for their financial sponsorship, as well as to Madeira Interactive Technology Institute, and Hotel Vidamar for hosting SustainIT 2017.

The work in this PhD thesis addresses the practical implications of deploying and testing Non-Intrusive Load Monitoring (NILM) and eco-feedback solutions in real-world scenarios. The contributions to this topic are centered around the design and development of NILM frameworks that have been deployed in the wild, supporting long-term research in ecofeedback and also serving the purpose of producing real-world datasets and furthering the state of the art regarding the performance metrics used to evaluate NILM algorithms. This thesis consists of three main parts: i) the development of tools and datasets for NILM and eco-feedback research, ii) the design, implementation and deployment of NILM and eco-feedback technologies in real world scenarios, and iii) an experimental comparison of performance metrics for event detection and event classification algorithms. In the first part we describe the Energy Monitoring and Disaggregation Data Format (EMD-DF) and the SustData and SustDataED public datasets. In second part we discuss the development and deployment of two hardware and software platforms in real households, to support eco-feedback research. We then report on more than five years of experience in deploying and maintaining such platforms. Our findings suggest that the main practical issues can be divided in two categories, technological (e.g., system installation) and social (e.g., maintaining a steady sample throughout the whole study). In the final part of this thesis we analyze experimentally the behavior of a number of performance metrics for event detection and event classification, identifying clusters and relationships between the different measures. Our results evidence some considerable differences in the behavior of the performance metrics when applied to the different problems.

This paper presents EnerSpectrum a standard wall electricity socket redesigned to present how much of the electricity being consumed is sourced from a renewable energy source. We build on studies that report an increase in consumer energy awareness and literacy when presented with the source of their electricity. The prototype was evaluated by 10 users using interviews, which found the concept interesting and valuable. We conclude this paper with an outline of the future work necessary to evaluate the EnerSpectrum in real live conditions.

In this paper we present and evaluate a semi- automatic labeling prototype to enable the creation of fully labeled energy disaggregation datasets from sub-metered data. Our results advocate in favor of our approach and show that it is possible to extract individual appliance transitions with considerable precision, as long as the individual appliance information is present in the sub-metered data, and its resolution is high enough.

In this paper we present our approach to create an end-to-end software platform to enable the creation of meaningful and systematic, cross-dataset performance evaluations and benchmarks of Non-Intrusive Load Monitoring technology. We specifically propose a new file format to represent public datasets, a software framework to implement algorithms and metrics as well as the application of ceiling analysis to evaluate the overall performance of NILM systems.

This paper presents the design, implementation and evaluation of an eco-feedback system capable of providing detailed household consumption information and also real-time production breakdown per energy source. We build on recent studies reporting an increased awareness generated by eco- feedback systems that also integrate micro-production information, taking advantage of a closed grid production network on an island with a high concentration of renewables, we deployed the What-a-Watt system in a building with 9 households for a period of 34 consecutive weeks. Results show that all the participating families have shown increased awareness of the production and distribution of electricity, thus becoming more familiarized with concepts such as the different sources of energy and how their availability relates to external variables such as weather conditions and time of day. Furthermore, our results also show, that the families using our system have managed to reduce their overall consumption. This research is a first attempt to provide more effective eco-feedback systems to consumers by integrating complex Smartgrid information in the feedback.

Avatars can be employed as a motivational tool, for example, allowing non-verbal communication that can be close to human communication. We describe two lab studies where we presented participants with avatars that communicated verbally via text and visually via expressions. In the first study, participants rated five different categories of captions and corresponding avatars. Results showed that the most persuasive, consistent and trustworthy verbal feedback was given in a humanized form. The second study was an exhaustive forced choice experiment where participants chose the happiest avatar from a pair displayed. Results showed participants found visual avatars more expressive and easier to understand than their verbal counterparts, and that users respond differently when presented with negative or positive emotions. This paper contributes to a better understanding of how to design feedback for expressive avatars.

In this paper we propose a common file format and API for public Non-Intrusive Load Monitoring (NILM) datasets such that researchers can easily evaluate their approaches across the different datasets and benchmark their results against prior work. The proposed file format enables storing the power demand of the whole house along with individual appliance consumption, and other relevant metadata in a single compact file, whereas the API supports the creation and manipulation of individual files and datasets in the proposed format.

Energy and environmental sustainability can benefit a lot from advances in data mining and machine learning techniques. However, those advances rely on the availability of relevant datasets required to develop, improve and validate new techniques. Only recently the first datasets were made publicly available for the energy and sustainability research community. In this paper we present a freely available dataset containing power usage and related information from 50 homes. Here we describe our dataset, the hardware and software setups used when collecting the data and how others can access it. We then discuss potential uses of this data in the future of energy eco- feedback and demand side management research.

This paper presents the design and implementation of an eco-feedback device, designed to give users information regarding energy production values with a particular focus on green and renewable energy. The system is installed in a 7’’ tablet and it can be used anywhere where an Internet connection is available. Furthermore the system allows users to visualize detailed information regarding their electricity consumption, such as averages by day, week and real time consumption. By combining this information we aim at informing the consumers about the availability of green or renewable sources of energy in real-time, thus stimulating awareness and helping them make informed decisions that can reduce the environmental impact of their everyday actions.

For the last couple of decades the world has been witnessing a change in habits of energy consumption in domestic environments, with electricity emerging as the main source of energy consumed. The effects of these changes in our eco-system are hard to assess, therefore encouraging researchers from different fields to conduct studies with the goal of understanding and improving perceptions and behaviors regarding household energy consumption. While several of these studies report success in increasing awareness, most of them are limited to short periods of time, thus resulting in a reduced knowledge of how householders will behave in the long-term. In this paper we attempt to reduce this gap presenting a long-term study on household electricity consumption. We deployed a real-time non-intrusive energy monitoring and eco-feedback system in 12 families during 52 weeks. Results show an increased awareness regarding electricity consumption despite a significant decrease in interactions with the eco-feedback system over time. We conclude that after one year of deployment of eco-feedback it was not possible to see any significant increase or decrease in the household consumption. Our results also confirm that consumption is tightly coupled with independent variables like the household size and the income-level of the families.

This paper reports a 15 weeks study of artistic eco-feedback deployed in six houses with an innovative sensing infrastructure and visualization strategy. The paper builds on previous work that showed a significant decrease in user awareness after a short period with a relapse in consumption. In this study we aimed to investigate if new forms of feedback could overcome this issue, maintaining the users awareness for longer periods of time. The study presented here aims at understanding if people are more aware of their energy consumption after the installation of a new, art inspired eco-feedback. The research question was then: does artistic eco-feedback provide an increased awareness over normal informative feedback? And does that awareness last longer? To answer this questions participants were interviewed and their consumption patterns analyzed. The main contribution of the paper is to advance our knowledge about the effectiveness of eco-feedback and provide guidelines for implementation of novel eco-feedback visualizations that overcome the relapse behavior pattern.

This paper describes a novel art-inspired tangible eco-feedback system. The concept emerged from a workshop with researchers, designers and artists looking at innovative ways to provide more effective eco-feedback that engages users emotionally. The tangible aspect of the system is composed of a set of magnets that users can stick on their physical mailbox outside of their apartment building according to their average energy consumption. The magnets are a total of seven pieces, one for each day of the week. Each piece has a variation of three colors, from green (low consumption) to burning red (high consumption). The magnets are to be displayed in a sequence that represents a typical panorama of local nature. In this paper we report the design and the study we conducted to gauge preliminary results on the system usage and potential. Interviews with participants revealed that none of them felt uncomfortable having their consumption displayed outside. When children were involved in the process they “took control” of the task and pressured their families to perform better.

Researchers often face engineering problems, such as optimizing prototype costs and ensuring easy access to the collected data, which are not directly related to the research problems being studied. This is especially true when dealing with long-term studies in real world scenarios. This paper describes the engineering perspective of the design, development and deployment of a long-term real word study on energy eco-feedback, where a non-intrusive home energy monitor was deployed in 30 houses for 18 months. Here we report on the efforts required to implement a cost-effective non-intrusive energy monitor and, in particular, the construction of a local network to allow remote access to multiple monitors and the creation of a RESTful web-service to enable the integration of these monitors with social media and mobile software applications. We conclude with initial results from a few eco-feedback studies that were performed using this platform.

This thesis presents a low cost non-intrusive home energy monitor built on top of Non-Intrusive Load Monitoring (NILM) concepts and techniques. NILM solutions are already considered low cost alternatives to the big majority of existing commercial energy monitors but the goal here is to make its cost even lower by using a mini netbook as a whole in one solution. The mini netbook is installed in the homes main circuit breaker and computes power consumption by reading current and voltage from the built-in sound card. At the same time, feedback to the users is provided using the 11’’ LCD screen as well as other built-in I/O modules. Our meter is also capable of detecting changes in power and tries to find out which appliance lead to that change and it is being used as part of an eco-feedback platform that was build to study the long terms of energy eco-feedback in individuals. In this thesis the steps that were taken to come up with such a system are presented, from the basics of AC power measurements to the implementation of an event detector and classifier that was used to disaggregate the power load. In the last chapter results from some validation tests that have been performed are presented in order to validate the experiment. It is believed that such a system will not only be important as an energy monitor, but also as an open system than can be easily changed to accommodate and test new or existing nonintrusive load monitoring techniques.